{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using Theano backend.\n"
     ]
    }
   ],
   "source": [
    "import keras\n",
    "from musicnet.dataset import MusicNet\n",
    "from keras.models import load_model\n",
    "\n",
    "%matplotlib inline\n",
    "import seaborn\n",
    "from matplotlib import pyplot as plt\n",
    "from sklearn.metrics import precision_recall_curve, average_precision_score\n",
    "\n",
    "from complexnn.conv import ComplexConv1D\n",
    "from complexnn.bn import ComplexBN, ComplexBatchNormalization\n",
    "from complexnn.init import sqrt_init\n",
    "from keras import initializers\n",
    "from keras.models import model_from_yaml\n",
    "setattr(initializers, \"sqrt_init\", sqrt_init)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "dataset = MusicNet('/Tmp/serdyuk/data/musicnet_11khz.npz', complex_=True, fourier=True)\n",
    "dataset.load()\n",
    "Xtest, Ytest = dataset.eval_set('test')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "model = model_from_yaml(open(\"models/complex_model.yaml\"),\n",
    "                   custom_objects={'ComplexConv1D': ComplexConv1D, \n",
    "                                   'ComplexBN': ComplexBN, \n",
    "                                   'sqrt_init': sqrt_init,\n",
    "                                   'ComplexBatchNormalization': ComplexBatchNormalization})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "model2 = model_from_yaml(open(\"models/real_model.yaml\"),\n",
    "                   custom_objects={'ComplexConv1D': ComplexConv1D, \n",
    "                                   'ComplexBN': ComplexBN, \n",
    "                                   'sqrt_init': sqrt_init,\n",
    "                                   'ComplexBatchNormalization': ComplexBatchNormalization})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "model.load_weights(\"models/complex_model.hdf5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "model2.load_weights(\"models/real_model.hdf5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "pr = model.predict(Xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "pr2 = model2.predict(Xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
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QoUPAYxjPV3I+RydO/dza5tb9GtedjmndT3V1tX/Z+pk7PU3e2j+N44BTakrr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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7faed09cb2d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "seaborn.set_style('white')\n",
    "seaborn.set_context('poster')\n",
    "plt.figure()\n",
    "plt.subplot(211)\n",
    "plt.imshow(pr[:200, 10:65].T)\n",
    "plt.yticks(list(range(10, 65, 10)))\n",
    "plt.ylabel('Note ID')\n",
    "\n",
    "plt.subplot(212)\n",
    "plt.imshow(Ytest[:200, 10:65].T)\n",
    "plt.yticks(list(range(10, 65, 10)))\n",
    "plt.xlabel('Frame')\n",
    "plt.ylabel('Note ID')\n",
    "plt.savefig(\"pred_gt.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Ix2YBuDqZaBrmQ9XKjiooREREbhAVDyI3kefaPEZuXi6fbPqW3+LWFxv3zpqP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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7faed0d4b210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "precision1, recall1, _ = precision_recall_curve(Ytest.flatten(), pr.flatten())\n",
    "precision2, recall2, _ = precision_recall_curve(Ytest.flatten(), pr2.flatten())\n",
    "seaborn.set_style('whitegrid')\n",
    "seaborn.set_context('poster')\n",
    "plt.figure()\n",
    "mod1, = plt.plot(recall1, precision1)\n",
    "mod2, = plt.plot(recall2, precision2)\n",
    "plt.xlabel('Recall')\n",
    "plt.ylabel('Precision')\n",
    "plt.legend([mod1, mod2], [\"Complex\", \"Real\"])\n",
    "plt.savefig('precision_recall.png')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
